A numerical study of fatigue life in non-Newtonian thermal EHL rolling–sliding contacts with spinning

This paper presents a study on fatigue life in non-Newtonian thermal elastohydrodynamic lubrication (TEHL) point contacts with spinning. A numerical procedure is developed and extended to rolling contact fatigue (RCF) life. The results show that the effect of entraining velocity on the RCF life is closely related to ellipticity. The RCF life first decreases steeply and then gradually with increase in slide–roll ratio. However, the RCF life may increase slightly at a large slide–roll ratio. Spinning is beneficial for reduction of longitudinal friction coefficient; however, even for smooth surface contact, the RCF life can be slightly reduced by spinning.     

TGP206在庙子梁隧道斜井地质超前预报中的应用

介绍了TGP206超前地质预报系统的原理和使用方法,同时,结合庙子梁隧道斜井施工实例,阐述了TGP206在隧道地质超前预报中的实用性和有效性,根据现场反馈的地质资料,证明了TGP206超前隧道预报仪具有较好的准确性,能够有效的指导工程施工。     

Modeling of degradation processes in historical mortars

The aim of presented paper is modeling of degradation processes in historical mortars exposed to moisture impact during freezing. Internal damage caused by ice crystallization in pores is one of the most important factors limiting the service life of historical structures. Coupling the transport processes with the mechanical part will allow us to address the impact of moisture on the durability, strength and stiffness of mortars. This should be accomplished with the help of a complex thermo-hygro-mechanical model representing one of the prime objectives of this work. The proposed formulation is based on the extension of the classical poroelasticity models with the damage mechanics. An example of two-dimensional moisture transport in the environment with temperature below freezing point is presented to support the theoretical derivations.     

Forecasting the realized volatility of the oil futures market: A regime switching approach

Considering nonlinear and highly persistent dynamics of realized volatility, we introduce Markov regime switching models to the Heterogeneous Autoregressive model of the Realized Volatility (HAR-RV) models to forecast the realized volatility of the crude oil futures market. In-sample results demonstrate that the high volatility regime is short-lived. Out-of-sample results suggest that HAR-RV models with regime switching increase the forecasting ability significantly than those without regime switching. Moreover, these findings are robust for different actual volatility benchmarks, forecasting windows, and model settings.     

Dynamic behaviour of high strength steel parts developed through laser assisted direct metal deposition

High strength steel alloys are good candidates for many engineering applications particularly those involving high strains and impact loads. Such applications in energy absorption devices require materials that can sustain dynamic loading and remain strong under demanding conditions. But the processing cost of these alloys has been a prohibitive factor, thus re-enforcing the research on porous and cellular structures made of stainless steels. Direct metal deposition (DMD) is a process which employs the power of a CO₂ laser to melt and deposit metallic powders onto steel substrates. Such structures offer advantages of creating novel configurations only by computer control of laser “tool path”. This research investigates the mechanical behaviour of solid and porous parts with prismatic cavities under quasi-static and dynamic compressive loading. Apart from two main deficiencies of relatively large variations of properties among the test specimen and sufficiently low modulus of elasticity, the stress strain behaviour is very close to the commercial grades of stainless steel produced by rolling and forming. The energy absorption behaviour of porous specimen is also very encouraging and renders DMD as a suitable process for manufacturing of customized sandwich and graded structures that can be used as a substitution for many engineering applications such as monolithic compression plates and explosion shields.     

Probabilistic load flow calculation with quasi-Monte Carlo and multiple linear regression

In this paper, quasi-Monte Carlo combined with multiple linear regression (QMC-MLR) is proposed to solve probabilistic load flow (PLF) calculation. A distinguishing feature of the paper is that PLF is approached by a low-dimensional problem with the concept of the effective dimension, and thus QMC based on low-discrepancy sequences is used to improve the sampling efficiency of the Monte Carlo simulation (MCS). Moreover, according to the relationship between linear correlation and linear regression, the MLR-based correlation control technique is developed to arrange the orders of samples in order to introduce prescribed dependences between variables. The proposed method is tested with the IEEE 118-bus system. Simulation results indicate that the MLR-based technique is robust and efficient in handling correlated non-normal variables and the proposed method shows better performances in PLF calculation compared with other MCS techniques, including simple random sampling (SRS), Latin hypercube sampling (LHS) and Latin supercube sampling (LSS).     

Forecasting crude-oil market volatility: Further evidence with jumps

This paper analyzes volatility models and their forecasting abilities in the presence of jumps in two crude-oil markets - Brent and West Texas Intermediate (WTI) - between January 6th 1992 and December 31st 2014. We compare a number of GARCH-type models that capture short memory as well as asymmetry (GARCH, GJR-GARCH and EGARCH), estimated on raw returns, to three competing approaches that deal with the presence of jumps: GARCH-type models estimated on jump-filtered returns, and two new classes of volatility models, called Generalized Autoregressive Score (GAS) and Markov-switching multifractal (MSM) models, estimated using raw returns. The forecasting performance of these volatility models is evaluated using the model confidence set approach, which allows us to identify a subset of models that outperform all the other competing models. We find that asymmetric models estimated on filtered returns provide better out-of-sample forecasts than do GARCH-, GAS-type and MSM models estimated on raw return series for Brent and WTI returns.     

Adhesion,plasticity and other peculiar properties of solid methane

Adhesion and plasticity of polycrystalline solid methane have been studied in the temperature range 10–90 K. It was shown that adhesion is very strong in the 50–90 K temperature range where the solid methane is soft and sticky. Closer to the melting point (at 90.6 K) the solid methane behaves as extremely viscous non-Newtonian liquid. Below 30 K solid methane loses its stickiness and ductility and acts like ordinary glass. Methane adhesion remains similar for very different materials. We have also made some video observations which reveal unexpected and mostly inexplicable properties of solid methane which could be of some interest for designing new methane moderators for neutron sources and planetary physics particularly the study of the environment of Titan.     

Dynamic computing rough approximations approach to time-evolving information granule interval-valued ordered information system

With the advent of Big Data era has seen both the volumes and update rates of data increase rapidly. The granular structure of an information system is evolving with time when redundancy data leaves and new data arrives. In order to quickly achieve the rough approximations of dynamic attribute set interval-valued ordered information system that the attribute set varies over time. In this study, we proposed two dynamic computing rough approximations approaches for time-evolving information granule interval-valued ordered information system which induced by the deletion or addition some attributes, respectively. The updating mechanisms enable obtaining additional knowledge from the varied data without forgetting the prior knowledge. According to these established computing rules, two corresponding dynamic computing algorithms are designed and some examples are illustrated to explain updating principles and show computing process. Furthermore, a series of experiments were conducted to evaluate the computational efficiency of the studied updating mechanisms based on several UCI datasets. The experimental results clearly indicate that these methods significantly outperform the traditional approaches with a dramatic reduction in the computational efficiency to update the rough approximations.